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__init__.py
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__init__.py
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# -*- coding: utf-8 -*-
# Copyright 2016-2023 Flensburg University of Applied Sciences,
# Europa-Universität Flensburg,
# Centre for Sustainable Energy Systems,
# DLR-Institute for Networked Energy Systems
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation; either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# File description
"""
execute.py defines optimization and simulation methods for the etrago object.
"""
import os
if "READTHEDOCS" not in os.environ:
import logging
import time
from pypsa.linopf import network_lopf
from pypsa.pf import sub_network_pf
import numpy as np
import pandas as pd
from etrago.tools.constraints import Constraints
logger = logging.getLogger(__name__)
__copyright__ = (
"Flensburg University of Applied Sciences, "
"Europa-Universität Flensburg, "
"Centre for Sustainable Energy Systems, "
"DLR-Institute for Networked Energy Systems"
)
__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__author__ = (
"ulfmueller, s3pp, wolfbunke, mariusves, lukasol, KathiEsterl, "
"ClaraBuettner, CarlosEpia, AmeliaNadal"
)
def update_electrical_parameters(network, l_snom_pre, t_snom_pre):
"""
Update electrical parameters of active branch components
considering s_nom of previous iteration.
Parameters
----------
network : pypsa.Network object
Container for all network components.
l_snom_pre: pandas.Series
s_nom of ac-lines in previous iteration.
t_snom_pre: pandas.Series
s_nom of transformers in previous iteration.
Returns
-------
None.
"""
network.lines.x[network.lines.s_nom_extendable] = (
network.lines.x * l_snom_pre / network.lines.s_nom_opt
)
network.transformers.x[network.transformers.s_nom_extendable] = (
network.transformers.x * t_snom_pre / network.transformers.s_nom_opt
)
network.lines.r[network.lines.s_nom_extendable] = (
network.lines.r * l_snom_pre / network.lines.s_nom_opt
)
network.transformers.r[network.transformers.s_nom_extendable] = (
network.transformers.r * t_snom_pre / network.transformers.s_nom_opt
)
network.lines.g[network.lines.s_nom_extendable] = (
network.lines.g * network.lines.s_nom_opt / l_snom_pre
)
network.transformers.g[network.transformers.s_nom_extendable] = (
network.transformers.g * network.transformers.s_nom_opt / t_snom_pre
)
network.lines.b[network.lines.s_nom_extendable] = (
network.lines.b * network.lines.s_nom_opt / l_snom_pre
)
network.transformers.b[network.transformers.s_nom_extendable] = (
network.transformers.b * network.transformers.s_nom_opt / t_snom_pre
)
# Set snom_pre to s_nom_opt for next iteration
l_snom_pre = network.lines.s_nom_opt.copy()
t_snom_pre = network.transformers.s_nom_opt.copy()
return l_snom_pre, t_snom_pre
def run_lopf(etrago, extra_functionality, method):
"""
Function that performs lopf with or without pyomo
Parameters
----------
etrago : etrago object
eTraGo containing all network information and a PyPSA network.
extra_functionality: dict
Define extra constranits.
method: dict
Choose 'n_iter' and integer for fixed number of iterations or
'threshold' and derivation of objective in percent for variable number
of iteration until the threshold of the objective function is reached.
Returns
-------
None.
"""
x = time.time()
if etrago.conduct_dispatch_disaggregation is not False:
# parameters defining the start and end per slices
no_slices = etrago.args["temporal_disaggregation"]["no_slices"]
skipped = etrago.network.snapshot_weightings.iloc[0].objective
transits = np.where(
etrago.network_tsa.snapshots.isin(
etrago.conduct_dispatch_disaggregation.index
)
)[0]
if method["pyomo"]:
# repeat the optimization for all slices
for i in range(0, no_slices):
# keep information on the initial state of charge for the
# respectng slice
initial = transits[i - 1]
soc_initial = etrago.conduct_dispatch_disaggregation.loc[
[etrago.network_tsa.snapshots[initial]]
].transpose()
etrago.network_tsa.storage_units.state_of_charge_initial = (
soc_initial
)
etrago.network_tsa.stores.e_initial = soc_initial
etrago.network_tsa.stores.e_initial.fillna(0, inplace=True)
# the state of charge at the end of each slice is set within
# split_dispatch_disaggregation_constraints in constraints.py
# adapt start and end snapshot of respecting slice
start = transits[i - 1] + skipped
end = transits[i] + (skipped - 1)
if i == 0:
start = 0
if i == no_slices - 1:
end = len(etrago.network_tsa.snapshots)
etrago.network_tsa.lopf(
etrago.network_tsa.snapshots[start : end + 1],
solver_name=etrago.args["solver"],
solver_options=etrago.args["solver_options"],
pyomo=True,
extra_functionality=extra_functionality,
formulation=etrago.args["model_formulation"],
)
if etrago.network_tsa.results["Solver"][0]["Status"] != "ok":
raise Exception("LOPF not solved.")
else:
for i in range(0, no_slices):
status, termination_condition = network_lopf(
etrago.network_tsa,
etrago.network_tsa.snapshots[start : end + 1],
solver_name=etrago.args["solver"],
solver_options=etrago.args["solver_options"],
extra_functionality=extra_functionality,
formulation=etrago.args["model_formulation"],
)
if status != "ok":
raise Exception("LOPF not solved.")
etrago.network_tsa.storage_units.state_of_charge_initial = 0
etrago.network_tsa.stores.e_initial = 0
else:
if method["pyomo"]:
etrago.network.lopf(
etrago.network.snapshots,
solver_name=etrago.args["solver"],
solver_options=etrago.args["solver_options"],
pyomo=True,
extra_functionality=extra_functionality,
formulation=etrago.args["model_formulation"],
)
if etrago.network.results["Solver"][0]["Status"] != "ok":
raise Exception("LOPF not solved.")
else:
status, termination_condition = network_lopf(
etrago.network,
solver_name=etrago.args["solver"],
solver_options=etrago.args["solver_options"],
extra_functionality=extra_functionality,
formulation=etrago.args["model_formulation"],
)
if status != "ok":
raise Exception("LOPF not solved.")
y = time.time()
z = (y - x) / 60
print("Time for LOPF [min]:", round(z, 2))
def iterate_lopf(
etrago,
extra_functionality,
method={"n_iter": 4, "pyomo": True},
):
"""
Run optimization of lopf. If network extension is included, the specified
number of iterations is calculated to consider reactance changes.
Parameters
----------
etrago : etrago object
eTraGo containing all network information and a PyPSA network.
extra_functionality: dict
Define extra constranits.
method: dict
Choose 'n_iter' and integer for fixed number of iterations or
'threshold' and derivation of objective in percent for variable number
of iteration until the threshold of the objective function is reached.
"""
args = etrago.args
path = args["csv_export"]
lp_path = args["lpfile"]
if (
args["temporal_disaggregation"]["active"] is True
and etrago.conduct_dispatch_disaggregation is False
):
if args["csv_export"]:
path = path + "/temporally_reduced"
if args["lpfile"]:
lp_path = lp_path[0:-3] + "_temporally_reduced.lp"
if etrago.conduct_dispatch_disaggregation is not False:
if args["lpfile"]:
lp_path = lp_path[0:-3] + "_dispatch_disaggregation.lp"
etrago.network_tsa.lines["s_nom"] = etrago.network.lines["s_nom_opt"]
etrago.network_tsa.lines["s_nom_extendable"] = False
etrago.network_tsa.links["p_nom"] = etrago.network.links["p_nom_opt"]
etrago.network_tsa.links["p_nom_extendable"] = False
etrago.network_tsa.transformers["s_nom"] = etrago.network.transformers[
"s_nom_opt"
]
etrago.network_tsa.transformers.s_nom_extendable = False
etrago.network_tsa.storage_units["p_nom"] = (
etrago.network.storage_units["p_nom_opt"]
)
etrago.network_tsa.storage_units["p_nom_extendable"] = False
etrago.network_tsa.stores["e_nom"] = etrago.network.stores["e_nom_opt"]
etrago.network_tsa.stores["e_nom_extendable"] = False
etrago.network_tsa.storage_units.cyclic_state_of_charge = False
etrago.network_tsa.stores.e_cyclic = False
args["snapshot_clustering"]["active"] = False
args["skip_snapshots"] = False
args["extendable"] = []
network = etrago.network_tsa
else:
network = etrago.network
# if network is extendable, iterate lopf
# to include changes of electrical parameters
if network.lines.s_nom_extendable.any():
# Initialise s_nom_pre (s_nom_opt of previous iteration)
# to s_nom for first lopf:
l_snom_pre = network.lines.s_nom.copy()
t_snom_pre = network.transformers.s_nom.copy()
# calculate fixed number of iterations
if "n_iter" in method:
n_iter = method["n_iter"]
for i in range(1, (1 + n_iter)):
run_lopf(etrago, extra_functionality, method)
if args["csv_export"]:
path_it = path + "/lopf_iteration_" + str(i)
etrago.export_to_csv(path_it)
if i < n_iter:
l_snom_pre, t_snom_pre = update_electrical_parameters(
network, l_snom_pre, t_snom_pre
)
# Calculate variable number of iterations until threshold of objective
# function is reached
if "threshold" in method:
run_lopf(etrago, extra_functionality, method)
diff_obj = network.objective * method["threshold"] / 100
i = 1
# Stop after 100 iterations to aviod unending loop
while i <= 100:
if i == 100:
print("Maximum number of iterations reached.")
break
l_snom_pre, t_snom_pre = update_electrical_parameters(
network, l_snom_pre, t_snom_pre
)
pre = network.objective
run_lopf(etrago, extra_functionality, method)
i += 1
if args["csv_export"]:
path_it = path + "/lopf_iteration_" + str(i)
etrago.export_to_csv(path_it)
if abs(pre - network.objective) <= diff_obj:
print("Threshold reached after " + str(i) + " iterations.")
break
else:
run_lopf(etrago, extra_functionality, method)
etrago.export_to_csv(path)
if args["lpfile"]:
network.model.write(lp_path)
return network
def lopf(self):
"""
Functions that runs lopf according to arguments.
Returns
-------
None.
"""
x = time.time()
self.conduct_dispatch_disaggregation = False
iterate_lopf(
self,
Constraints(
self.args, self.conduct_dispatch_disaggregation
).functionality,
method=self.args["method"],
)
y = time.time()
z = (y - x) / 60
logger.info("Time for LOPF [min]: {}".format(round(z, 2)))
if self.args["csv_export"]:
path = self.args["csv_export"]
if self.args["temporal_disaggregation"]["active"] is True:
path = path + "/temporally_reduced"
self.export_to_csv(path)
def optimize(self):
"""Run optimization of dispatch and grid and storage expansion based on
arguments
Returns
-------
None.
"""
if self.args["method"]["type"] == "lopf":
self.lopf()
elif self.args["method"]["type"] == "market_grid": # besseren Namen finden
self.market_optimization()
# self.market_results_to_grid()
self.grid_optimization()
elif self.args["method"]["type"] == "sclopf":
self.sclopf(
post_lopf=False,
n_process=4,
delta=0.01,
n_overload=0,
div_ext_lines=False,
)
else:
print("Method not defined")
def dispatch_disaggregation(self):
"""
Function running the tempral disaggregation meaning the optimization
of dispatch in the temporally fully resolved network; therfore, the problem
is reduced to smaller subproblems by slicing the whole considered time span
while keeping inforation on the state of charge of storage units and stores
to ensure compatibility and to reproduce saisonality.
Returns
-------
None.
"""
if self.args["temporal_disaggregation"]["active"]:
x = time.time()
if self.args["temporal_disaggregation"]["no_slices"]:
# split dispatch_disaggregation into subproblems
# keep some information on soc in beginning and end of slices
# to ensure compatibility and to reproduce saisonality
# define number of slices and corresponding slice length
no_slices = self.args["temporal_disaggregation"]["no_slices"]
slice_len = int(len(self.network.snapshots) / no_slices)
# transition snapshots defining start and end of slices
transits = self.network.snapshots[0::slice_len]
if len(transits) > 1:
transits = transits[1:]
if transits[-1] != self.network.snapshots[-1]:
transits = transits.insert(
(len(transits)), self.network.snapshots[-1]
)
# for stores, exclude emob and dsm because of their special
# constraints
sto = self.network.stores[
~self.network.stores.carrier.isin(
["battery_storage", "battery storage", "dsm"]
)
]
# save state of charge of storage units and stores at those
# transition snapshots
self.conduct_dispatch_disaggregation = pd.DataFrame(
columns=self.network.storage_units.index.append(sto.index),
index=transits,
)
for storage in self.network.storage_units.index:
self.conduct_dispatch_disaggregation[storage] = (
self.network.storage_units_t.state_of_charge[storage]
)
for store in sto.index:
self.conduct_dispatch_disaggregation[store] = (
self.network.stores_t.e[store]
)
extra_func = self.args["extra_functionality"]
self.args["extra_functionality"] = {}
load_shedding = self.args["load_shedding"]
if not load_shedding:
self.args["load_shedding"] = True
self.load_shedding(temporal_disaggregation=True)
iterate_lopf(
self,
Constraints(
self.args, self.conduct_dispatch_disaggregation
).functionality,
method=self.args["method"],
)
# switch to temporally fully resolved network as standard network,
# temporally reduced network is stored in network_tsa
network1 = self.network.copy()
self.network = self.network_tsa.copy()
self.network_tsa = network1.copy()
network1 = 0
# keep original settings
if self.args["temporal_disaggregation"]["no_slices"]:
self.args["extra_functionality"] = extra_func
self.args["load_shedding"] = load_shedding
self.network.lines["s_nom_extendable"] = self.network_tsa.lines[
"s_nom_extendable"
]
self.network.links["p_nom_extendable"] = self.network_tsa.links[
"p_nom_extendable"
]
self.network.transformers.s_nom_extendable = (
self.network_tsa.transformers.s_nom_extendable
)
self.network.storage_units["p_nom_extendable"] = (
self.network_tsa.storage_units["p_nom_extendable"]
)
self.network.stores["e_nom_extendable"] = self.network_tsa.stores[
"e_nom_extendable"
]
self.network.storage_units.cyclic_state_of_charge = (
self.network_tsa.storage_units.cyclic_state_of_charge
)
self.network.stores.e_cyclic = self.network_tsa.stores.e_cyclic
if not self.args["csv_export"]:
path = self.args["csv_export"]
self.export_to_csv(path)
self.export_to_csv(path + "/temporal_disaggregaton")
y = time.time()
z = (y - x) / 60
logger.info("Time for LOPF [min]: {}".format(round(z, 2)))
def import_gen_from_links(network, drop_small_capacities=True):
"""
create gas generators from links in order to not lose them when
dropping non-electric carriers
"""
if drop_small_capacities:
# Discard all generators < 1kW
discard_gen = network.links[network.links["p_nom"] <= 0.001].index
network.links.drop(discard_gen, inplace=True)
for df in network.links_t:
if not network.links_t[df].empty:
network.links_t[df].drop(
columns=discard_gen.values, inplace=True, errors="ignore"
)
# Select links that should be represented as generators
gas_to_add = network.links[
network.links.carrier.isin(
[
"central_gas_CHP",
"OCGT",
"H2_to_power",
"industrial_gas_CHP",
]
)
].copy()
# Rename bus1 column to bus
gas_to_add.rename(columns={"bus1": "bus"}, inplace=True)
# Aggregate new generators per bus and carrier
df = pd.DataFrame()
df["p_nom"] = gas_to_add.groupby(["bus", "carrier"]).p_nom.sum()
df["p_nom_opt"] = gas_to_add.groupby(["bus", "carrier"]).p_nom_opt.sum()
df["marginal_cost"] = gas_to_add.groupby(
["bus", "carrier"]
).marginal_cost.mean()
df["efficiency"] = gas_to_add.groupby(["bus", "carrier"]).efficiency.mean()
df["control"] = "PV"
df.reset_index(inplace=True)
if not df.empty:
df.index = df.bus + " " + df.carrier
# Aggregate disptach time series for new generators
gas_to_add["bus1_carrier"] = gas_to_add.bus + " " + gas_to_add.carrier
if not network.links_t.p1.empty:
df_t = (
network.links_t.p1[gas_to_add.index]
.groupby(gas_to_add.bus1_carrier, axis=1)
.sum()
* -1
)
# Insert aggregated generators their dispatch time series
network.madd("Generator", df.index, **df)
if not network.links_t.p1.empty:
network.import_series_from_dataframe(df_t, "Generator", "p")
network.import_series_from_dataframe(
pd.DataFrame(index=df_t.index, columns=df_t.columns, data=1.0),
"Generator",
"status",
)
# Drop links now modelled as generator
network.mremove("Link", gas_to_add.index)
return
def run_pf_post_lopf(self):
"""
Function that runs pf_post_lopf according to arguments.
Returns
-------
None.
"""
if self.args["pf_post_lopf"]["active"]:
pf_post_lopf(self)
def pf_post_lopf(etrago, calc_losses=False):
"""
Function that prepares and runs non-linar load flow using PyPSA pf.
If crossborder lines are DC-links, pf is only applied on german network.
Crossborder flows are still considerd due to the active behavior of links.
To return a network containing the whole grid, the optimised solution of
the foreign components can be added afterwards.
Parameters
----------
etrago : etrago object
eTraGo containing all network information and a PyPSA network.
add_foreign_lopf: boolean
Choose if foreign results of lopf should be added to the network when
foreign lines are DC.
q_allocation: str
Choose allocation of reactive power. Possible settings are listed in
distribute_q function.
calc_losses: bolean
Choose if line losses will be calculated.
Returns
-------
"""
def drop_foreign_components(network):
"""
Function that drops foreign components which are only connected via
DC-links and saves their optimization results in pd.DataFrame.
Parameters
----------
network : pypsa.Network object
Container for all network components.
Returns
-------
None.
"""
# Create series for constant loads
constant_loads = network.loads[network.loads.p_set != 0]["p_set"]
for load in constant_loads.index:
network.loads_t.p_set[load] = constant_loads[load]
network.loads.p_set = 0
n_bus = pd.Series(index=network.sub_networks.index)
for i in network.sub_networks.index:
n_bus[i] = len(network.buses.index[network.buses.sub_network == i])
sub_network_DE = n_bus.index[n_bus == n_bus.max()]
foreign_bus = network.buses[
(network.buses.sub_network != sub_network_DE.values[0])
& (network.buses.country != "DE")
]
foreign_comp = {
"Bus": network.buses[network.buses.index.isin(foreign_bus.index)],
"Generator": network.generators[
network.generators.bus.isin(foreign_bus.index)
],
"Load": network.loads[network.loads.bus.isin(foreign_bus.index)],
"Transformer": network.transformers[
network.transformers.bus0.isin(foreign_bus.index)
],
"StorageUnit": network.storage_units[
network.storage_units.bus.isin(foreign_bus.index)
],
"Store": network.stores[
network.stores.bus.isin(foreign_bus.index)
],
}
foreign_series = {
"Bus": network.buses_t.copy(),
"Generator": network.generators_t.copy(),
"Load": network.loads_t.copy(),
"Transformer": network.transformers_t.copy(),
"StorageUnit": network.storage_units_t.copy(),
"Store": network.stores_t.copy(),
}
for comp in sorted(foreign_series):
attr = sorted(foreign_series[comp])
for a in attr:
if (
not foreign_series[comp][a].empty
and not (foreign_series[comp][a] == 0.0).all().all()
):
if a != "p_max_pu":
if a in ["q_set", "e_max_pu", "e_min_pu"]:
g_in_q_set = foreign_comp[comp][
foreign_comp[comp].index.isin(
foreign_series[comp][a].columns
)
]
foreign_series[comp][a] = foreign_series[comp][a][
g_in_q_set.index
]
else:
foreign_series[comp][a] = foreign_series[comp][a][
foreign_comp[comp].index
]
else:
foreign_series[comp][a] = foreign_series[comp][a][
foreign_comp[comp][
foreign_comp[comp].index.isin(
network.generators_t.p_max_pu.columns
)
].index
]
# Drop components
network.buses = network.buses.drop(foreign_bus.index)
network.generators = network.generators[
network.generators.bus.isin(network.buses.index)
]
network.loads = network.loads[
network.loads.bus.isin(network.buses.index)
]
network.transformers = network.transformers[
network.transformers.bus0.isin(network.buses.index)
]
network.storage_units = network.storage_units[
network.storage_units.bus.isin(network.buses.index)
]
network.stores = network.stores[
network.stores.bus.isin(network.buses.index)
]
return foreign_bus, foreign_comp, foreign_series
x = time.time()
network = etrago.network
args = etrago.args
network.lines.s_nom = network.lines.s_nom_opt
# generators modeled as links are imported to the generators table
import_gen_from_links(network)
if args["spatial_disaggregation"]:
import_gen_from_links(
etrago.disaggregated_network, drop_small_capacities=False
)
# For the PF, set the P to be the optimised P
network.generators_t.p_set = network.generators_t.p_set.reindex(
columns=network.generators.index
)
network.generators_t.p_set = network.generators_t.p
network.storage_units_t.p_set = network.storage_units_t.p_set.reindex(
columns=network.storage_units.index
)
network.storage_units_t.p_set = network.storage_units_t.p
network.stores_t.p_set = network.stores_t.p_set.reindex(
columns=network.stores.index
)
network.stores_t.p_set = network.stores_t.p
network.links_t.p_set = network.links_t.p_set.reindex(
columns=network.links.index
)
network.links_t.p_set = network.links_t.p0
network.determine_network_topology()
# if foreign lines are DC, execute pf only on sub_network in Germany
if (args["foreign_lines"]["carrier"] == "DC") or (
(args["scn_extension"] is not None)
and ("BE_NO_NEP 2035" in args["scn_extension"])
):
foreign_bus, foreign_comp, foreign_series = drop_foreign_components(
network
)
# Assign generators control strategy
ac_bus = network.buses[network.buses.carrier == "AC"]
network.generators.control[network.generators.bus.isin(ac_bus.index)] = (
"PV"
)
network.generators.control[
network.generators.carrier == "load shedding"
] = "PQ"
# Assign storage units control strategy
network.storage_units.control[
network.storage_units.bus.isin(ac_bus.index)
] = "PV"
# Find out the name of the main subnetwork
main_subnet = str(network.buses.sub_network.value_counts().argmax())
# Delete very small p_set and q_set values to avoid problems when solving
network.generators_t["p_set"][
np.abs(network.generators_t["p_set"]) < 0.001
] = 0
network.generators_t["q_set"][
np.abs(network.generators_t["q_set"]) < 0.001
] = 0
network.loads_t["p_set"][np.abs(network.loads_t["p_set"]) < 0.001] = 0
network.loads_t["q_set"][np.abs(network.loads_t["q_set"]) < 0.001] = 0
network.storage_units_t["p_set"][
np.abs(network.storage_units_t["p_set"]) < 0.001
] = 0
network.storage_units_t["q_set"][
np.abs(network.storage_units_t["p_set"]) < 0.001
] = 0
# execute non-linear pf
pf_solution = sub_network_pf(
sub_network=network.sub_networks["obj"][main_subnet],
snapshots=network.snapshots,
use_seed=True,
distribute_slack=True,
)
pf_solve = pd.DataFrame(index=pf_solution[0].index)
pf_solve["converged"] = pf_solution[2].values
pf_solve["error"] = pf_solution[1].values
pf_solve["n_iter"] = pf_solution[0].values
if not pf_solve[~pf_solve.converged].count().max() == 0:
logger.warning(
"PF of %d snapshots not converged.",
pf_solve[~pf_solve.converged].count().max(),
)
if calc_losses:
calc_line_losses(network, pf_solve["converged"])
network = distribute_q(
network, etrago.args["pf_post_lopf"]["q_allocation"]
)
y = time.time()
z = (y - x) / 60
print("Time for PF [min]:", round(z, 2))
# if selected, copy lopf results of neighboring countries to network
if (
(args["foreign_lines"]["carrier"] == "DC")
or (
(args["scn_extension"] is not None)
and ("BE_NO_NEP 2035" in args["scn_extension"])
)
) and etrago.args["pf_post_lopf"]["add_foreign_lopf"]:
for comp in sorted(foreign_series):
network.import_components_from_dataframe(foreign_comp[comp], comp)
for attr in sorted(foreign_series[comp]):
network.import_series_from_dataframe(
foreign_series[comp][attr], comp, attr
)
if args["csv_export"]:
path = args["csv_export"] + "/pf_post_lopf"
etrago.export_to_csv(path)
pf_solve.to_csv(os.path.join(path, "pf_solution.csv"), index=True)
if args["spatial_disaggregation"]:
etrago.disaggregated_network.export_to_csv_folder(
path + "/disaggregated_network"
)
return network
def distribute_q(network, allocation="p_nom"):
"""
Function that distributes reactive power at bus to all installed
generators and storages.
Parameters
----------
network : pypsa.Network object
Container for all network components.
allocation: str
Choose key to distribute reactive power:
'p_nom' to dirstribute via p_nom
'p' to distribute via p_set.
Returns
-------
None.
"""
ac_bus = network.buses[network.buses.carrier == "AC"]
gen_elec = network.generators[
(network.generators.bus.isin(ac_bus.index))
& (network.generators.carrier != "load shedding")
].carrier.unique()
network.allocation = allocation
if allocation == "p":
if (network.buses.carrier == "AC").all():
p_sum = (
network.generators_t["p"]
.groupby(network.generators.bus, axis=1)
.sum()
.add(
network.storage_units_t["p"]
.abs()
.groupby(network.storage_units.bus, axis=1)
.sum(),
fill_value=0,
)
)
q_sum = (
network.generators_t["q"]
.groupby(network.generators.bus, axis=1)
.sum()
)
q_distributed = (
network.generators_t.p
/ p_sum[network.generators.bus.sort_index()].values
* q_sum[network.generators.bus.sort_index()].values
)
q_storages = (
network.storage_units_t.p
/ p_sum[network.storage_units.bus.sort_index()].values
* q_sum[network.storage_units.bus.sort_index()].values
)
else:
print(
"""WARNING: Distribution of reactive power based on active
power is currently outdated for sector coupled models. This
process will continue with the option allocation = 'p_nom'"""
)
allocation = "p_nom"
if allocation == "p_nom":
q_bus = (
network.generators_t["q"]
.groupby(network.generators.bus, axis=1)
.sum()
.add(
network.storage_units_t.q.groupby(
network.storage_units.bus, axis=1
).sum(),
fill_value=0,
)
)
total_q1 = q_bus.sum().sum()
ac_bus = network.buses[network.buses.carrier == "AC"]
gen_elec = network.generators[
(network.generators.bus.isin(ac_bus.index))
& (network.generators.carrier != "load shedding")
& (network.generators.p_nom > 0)
].sort_index()
q_distributed = q_bus[gen_elec.bus].multiply(gen_elec.p_nom.values) / (
(
gen_elec.p_nom.groupby(network.generators.bus)
.sum()